Method and system for classifying one or more images

ABSTRACT

A method for determining a predictability of a media entity portion, the method includes: receiving or generating (a) reference media descriptors, and (b) probability estimations of descriptor space representatives given the reference media descriptors; wherein the descriptor space representatives are representative of a set of media entities; and calculating a predictability score of the media entity portion based on at least (a) the probability estimations of the descriptor space representatives given the reference media descriptors, and (b) relationships between the media entity portion descriptors and the descriptor space representatives. A method for processing media streams, the method may include: applying probabilistic non-parametric process on the media stream to locate media portions of interest; and generating metadata indicative of the media portions of interest.

RELATED APPLICATIONS

This application is a continuation in part of U.S. patent applicationSer. No. 13/041,457 filing date Mar. 7, 2011 which in turn claims thepriority of US provisional patent filing date Mar. 8, 2010 and Ser. No.61/311,524. This application claims the priority of U.S. provisionalpatent Ser. No. 61/374,671 filing date Aug. 18, 2010, all of which areincorporated herein by reference.

BACKGROUND

In recent years, we are all facing an explosion of visual informationincluding images and video. Some of this visual information consists ofoffending content or other content that is inappropriate to watch bysome sectors of the population, such as by children.

The need for content moderation has increased in recent years, since theInternet is now available for everyone, even children at young ages.

Existing solutions are based mostly on textual analysis or internetdomain filtering. However, with the emerging of Web 2.0 and UserGenerated Content sites (such as YouTube, FaceBook, Flickr) thetraditional solutions are insufficient. In many cases, the visualinformation is not accompanied with any textual information. In othercases, misleading textual information is inserted deliberately. Thisrenders textual analysis and domain filtering useless against thesethreats. Other methods such as crowd sourcing and collaborativefiltering are inefficient for the long tail of visual information inuser generated content.

Section 2: Related and Previous Work

There has been some work in the field of content moderation andfiltering based on visual analysis. Traditional methods use mainlysimple image features such as skin information (such as in (1)), orcombine skin information with texture and color histograms (See forexample (2)).

More recent methods uses a stronger model known as the “Bag-of-features”model, where one creates a dictionary of quantized image descriptors(such as Sift (3)) and then statistical tools such as SVM or PLSA areused to learn a model of porn/non-porn images from the dictionaries (4).Some effort has been put in improving the used image descriptors (e.g.(5)), improving the learning method (e.g. —using PLSA as suggested by(5)) and improving the run-time (e.g. by using image features that canbe computed very fast, e.g. amount of edges (6)).

These methods share the model/parametric-based approach.

SUMMARY OF THE INVENTION

A method for classifying at least one image, the method may include:partitioning the at least one image to multiple media entities orreceiving a partition information indicative of a partition of the atleast one image to multiple media entities; receiving or generating (a)media class descriptors for each media entity class out of a set ofmedia entity classes, and (b) probability estimations of descriptorspace representatives given each of the media entity classes; whereinthe descriptor space representatives are representative of a set ofmedia entities; calculating, for each pair of media entity and mediaclass, a predictability score based on (a) the probability estimationsof the descriptor space representatives given the media classdescriptors of the media class, and (b) relationships betweendescriptors of the media entity and the descriptor spacerepresentatives; classifying each media entity of the multiple mediaentities based on predictability scores of the media entity given eachmedia class; and providing at least one image classification, based onclasses of each of the multiple media entities.

The at least one image may include multiple images and wherein theclassifying may include classifying each image.

The at least one image may form a video stream.

The at least one image can be a single image.

The method may include applying a human body detection algorithm on theat least one image to provide human body detection results; andpartitioning the at least one image based on the human body detectionresults.

The method may include applying a face detection algorithm on the atleast one image to provide face detection results; and partitioning theat least one image based on the face detection results.

The method may include applying a human skin detection algorithm on theat least one image to provide human skin detection results; andpartitioning the at least one image based on the human skin detectionresults.

The method may include determining whether the at least one image shouldbe prevented from being displayed.

The method may include defining a dominant class and classifying animage out of the at least one image as belonging to the dominant classif at least one media entity of the image is classified as belonging tothe dominant class.

The method may include executing multiple iterations of a sequence ofstages that comprises the stages of receiving or generating,calculating, and classifying; wherein different iterations differ fromeach other by accuracy of execution and speed of execution.

The decision of whether to apply each iteration may be based on theoutput of previous iterations.

The method may include filtering out an image based on an outcome of atleast one iteration of the multiple iterations.

The method may include detecting a human organ; determining, based on alocation of the human organ, an expected location of at least one otherhuman organ; and verifying the expected location of the at least oneother human organ by processing at least one portion of an image thatcorresponds to the expected location of the at least one other humanorgan.

The method may include executing multiple iterations of a sequence ofstages that comprises the stages of receiving or generating,calculating, and classifying; wherein different iterations differ fromeach other by a complexity level.

At least two iterations may further include partitioning the at leastone image.

The method may include performing multiple iterations, each iterationassociated with a higher complexity level.

The decision of whether to apply each iteration may be based on theoutput of previous iterations.

The method may include executing multiple iterations of a sequence ofstages that comprises the stages of receiving or generating,calculating, and classifying; wherein different iterations differ fromeach other by a number of descriptors of the media entities.

At two iterations may include partitioning the at least one image.

The method may include performing multiple iterations while increasingthe number of descriptors of the media entities.

The method may include executing multiple iterations of a sequence ofstages that comprises the stages of receiving or generating,calculating, and classifying; wherein different iterations differ fromeach other by a selection of media class descriptors that are taken intoaccount during the iteration.

At least two iterations may include partitioning the at least one image.

The method may include executing multiple iterations of a sequence ofstages that comprises the stages of receiving or generating,calculating, and classifying; wherein different iterations differ fromeach other by descriptors of the media entity that are taken intoaccount during the iteration.

At least two iterations may include partitioning the at least one image.

The method may include applying a motion detection algorithm on the atleast one image to provide motion detection results; and partitioningthe at least one image based on the motion detection results.

A computer program product that comprises a non-transitory computerreadable medium that stores instructions for: partitioning the at leastone image to multiple media entities or receiving a partitioninformation indicative of a partition of the at least one image tomultiple media entities; receiving or generating (a) media classdescriptors for each media entity class out of a set of media entityclasses, and (b) probability estimations of descriptor spacerepresentatives given each of the media entity classes; wherein thedescriptor space representatives are representative of a set of mediaentities; calculating, for each pair of media entity and media class, apredictability score based on (a) the probability estimations of thedescriptor space representatives given the media class descriptors ofthe media class, and (b) relationships between descriptors of the mediaentity and the descriptor space representatives; classifying each mediaentity of the multiple media entities based on predictability scores ofthe media entity given each media class; and providing at least oneimage classification, based on classes of each of the multiple mediaentities.

The at least one image may include multiple images and wherein theclassifying may include classifying each image.

The at least one image may form a video stream.

The at least one image can be a single image.

The computer program produce may further store instructions for applyinga human body detection algorithm on the at least one image to providehuman body detection results; and partitioning the at least one imagebased on the human body detection results.

The computer program produce may further store instructions for applyinga face detection algorithm on the at least one image to provide facedetection results; and partitioning the at least one image based on theface detection results.

The computer program produce may further store instructions for applyinga human skin detection algorithm on the at least one image to providehuman skin detection results; and partitioning the at least one imagebased on the human skin detection results.

The computer program produce may further store instructions fordetermining whether the at least one image should be prevented frombeing displayed.

The computer program produce may further store instructions for defininga dominant class and classifying an image out of the at least one imageas belonging to the dominant class if at least one media entity of theimage is classified as belonging to the dominant class.

The computer program produce may further store instructions forexecuting multiple iterations of a sequence of stages that comprises thestages of receiving or generating, calculating, and classifying; whereindifferent iterations differ from each other by accuracy of execution andspeed of execution.

The decision of whether to apply each iteration may be based on theoutput of previous iterations.

The computer program produce may further store instructions forfiltering out an image based on an outcome of at least one iteration ofthe multiple iterations.

The computer program produce may further store instructions fordetecting a human organ; determining, based on a location of the humanorgan, an expected location of at least one other human organ; andverifying the expected location of the at least one other human organ byprocessing at least one portion of an image that corresponds to theexpected location of the at least one other human organ.

The computer program produce may further store instructions forexecuting multiple iterations of a sequence of stages that comprises thestages of receiving or generating, calculating, and classifying; whereindifferent iterations differ from each other by a complexity level.

At least two iterations may further include partitioning the at leastone image.

The computer program produce may further store instructions forperforming multiple iterations, each iteration associated with a highercomplexity level.

The decision of whether to apply each iteration may be based on theoutput of previous iterations.

The computer program produce may further store instructions forexecuting multiple iterations of a sequence of stages that comprises thestages of receiving or generating, calculating, and classifying; whereindifferent iterations differ from each other by a number of descriptorsof the media entities.

At two iterations may include partitioning the at least one image.

The computer program produce may further store instructions forperforming multiple iterations while increasing the number ofdescriptors of the media entities.

The computer program produce may further store instructions forexecuting multiple iterations of a sequence of stages that comprises thestages of receiving or generating, calculating, and classifying; whereindifferent iterations differ from each other by a selection of mediaclass descriptors that are taken into account during the iteration.

At least two iterations may include partitioning the at least one image.

The computer program produce may further store instructions forexecuting multiple iterations of a sequence of stages that comprises thestages of receiving or generating, calculating, and classifying; whereindifferent iterations differ from each other by descriptors of the mediaentity that are taken into account during the iteration.

At least two iterations may include partitioning the at least one image.

The computer program produce may further store instructions for applyinga motion detection algorithm on the at least one image to provide motiondetection results; and partitioning the at least one image based on themotion detection results.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 illustrates a system according to an embodiment of the invention;

FIG. 2 illustrates a system and its environment according to anembodiment of the invention;

FIG. 3 illustrates a method according to an embodiment of the invention;

FIG. 4 illustrates a pre-processing block according to an embodiment ofthe invention;

FIG. 5 illustrates a query block according to an embodiment of theinvention;

FIG. 6 illustrates a similarity block according to an embodiment of theinvention;

FIG. 7 illustrates a classification block according to an embodiment ofthe invention;

FIG. 8 illustrates a clustering block according to an embodiment of theinvention;

FIG. 9 illustrates a SalienSee block according to an embodiment of theinvention;

FIG. 10 illustrates a detection block according to an embodiment of theinvention;

FIG. 11 illustrates an editing process according to an embodiment of theinvention;

FIG. 12 illustrates a system and its environment according to anembodiment of the invention;

FIG. 13-17 illustrate methods according to an embodiment of theinvention;

FIG. 18 illustrates a cascaded process according to an embodiment of theinvention;

FIG. 19 illustrates a method according to an embodiment of theinvention;

FIG. 20 illustrates a method according to an embodiment of theinvention;

FIG. 21 illustrates a method according to an embodiment of theinvention;

FIGS. 22A-22F illustrate an example of an image and various outcomes ofvarious processing stages according to an embodiment of the invention;

FIGS. 23-25 illustrate various systems according to various embodimentsof the invention; and

FIGS. 26-27 illustrate a detection of body organs based on detection ofanother body organ.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE DRAWINGS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

The illustrated methods, systems and computer program products mayprovide a comprehensive solution to the problems of browsing, searchingediting and producing personal video, by utilizing automatic image andvideo content analysis. In contrast to previous related art, themethods, systems and computer program products may identify all therequired aspects of the problem and thereby provides a completesolution.

The term media entity refers to information representative of visualinformation, information representative of audio information or acombination thereof. Non-limiting examples of a media entity may includean image, a video stream, an access unit, multiple images, a portion ofan image, a portion of a video stream, a transport packet, a elementarystream, a packetized elementary stream, an audio stream, an audio frame,any combination of audio representative information.

Any reference to a method should be interpreted as a reference to asystem and additionally or alternatively as a reference to a computerprogram product. Thus, when describing a method is it noted that themethod can be executed by a system or by a computer that executesinstructions of the computer program product.

Any reference to a system should be interpreted as a reference to amethod executed by the system and additionally or alternatively as areference to a computer program product. Thus, when describing a systemis it noted that the system can execute a method or can executeinstructions of the computer program product.

Any reference to a block can include a reference to a hardware block, asoftware block or a stage of a method. Thus, for example, any of theblocks illustrated in FIG. 4-9 can be regarded as method stages.

The methods, systems and computer program products may provide a unifiedand generic approach—the media predictability framework—for handling thenumerous capabilities required for a comprehensive solution.

Thus, instead of multiple ad hoc modules and partial solutions, themethods, systems and computer program products may provide provides asingle coherent approach to tackle the entire problem.

The methods, systems and computer program products can be applied indiverse technological environments.

Methods, systems and computer program products may provide acomprehensive solution for using personal video as they enablesbrowsing, searching editing and production of personal video.

The methods, systems and computer program products may rely on a unifiedautomated media content analysis method, instead of relying on numerousmethods for implementing the long list of features required for ‘mediaunderstanding’. The proposed method relies on a unified content analysisplatform that is based on the Media Predictability Framework (discussedin the next section), which forms the technological foundation of theproduct.

In this section we discuss the various type of meta-data (and their use)obtained using analysis with the media predictability framework.

The processing of media entities may involve running software componentson various hardware components and the processing of data files inseveral internet locations. We use the following entities in the textbelow:

User Computer: A computer with general computing capabilities such asDesktop, Laptop, Tablet, Media Center, Smartphone.

Personal Media: Images and Video of any common format (e.g., For images:Jpeg, Tiff, Gif, Jpeg2000 etc. For Video: Avi, wmv, mpeg-4, QuickTimeetc.)

Private Data and Meta-Data Database: Binary and Textual data andmeta-data kept in tables and files either as a flat databaseorganization or as a relational database (e.g., MySql).

Interaction Server: An online server (either dedicated or in a computingcloud) which handles at least one of: uploading of user media,streaming, recording usage and viewing analytics, handling user andvisitor interaction and registration, handling online payment, storageof online data and meta-data, selecting ads per viewed video and peruser/visitor.

Content Analysis Server: A server which performs content analysis foruploaded user media (user video including audio, user images, userselected soundtrack)

Production Server: A server, which utilizes the original footage and theanalyzed meta-data to create various personalized and stylized videoproductions. This server may utilize professional video creativesoftware such as Adobe After Effects, Sony Vegas etc. to render thevideo production (e.g., video effects and transitions).

Online Data and Meta-Data Database: An online database, which containsBinary and Textual data and meta-data kept in tables and files either asa flat database organization or as a relational database (e.g., MySql).

User Interface Application: A standalone application or web application(runs inside a web browser) or a software widget or software gadgetwhich enables the user to (at least one of) play, view, browse, search,produce, upload, broadcast and share his personal media.

Mobile Application: An application designed for a mobile device (e.g.,Cellular application, iPad application etc.). This application is aspecialized user interface application for the respective mobile device.

Local Player—A mini-version of the User Interface Application withreduced capabilities, which runs locally on the user/visitor computingdevice using a playing platform (e.g., Flash, Silverlight, HTML5).

Electronic Media Capturing Device—An electronic device which can capturepersonal image and/or video such as: Camcorder, Still Camera,Camera-phone, Internet Camera, Network Camera, Camera embedded in UserComputer (e.g., Laptop) etc.

‘My Video; My Pictures’ any set of file directories or libraries whichreside on the user computer (e.g, on a Hard drive, or anyelectro-magnetic or optical media such as DVD, CD, Blue-Ray disk,Flash-Memory etc.) or on the user online folders (e.g., DropBox) andwhich stores the user personal media or shared media.

FIG. 1 illustrates a interaction server 10, a user computer 20 and imageacquisition devices 31-33 according to an embodiment of the invention.

The user provides acquired media from image acquisition devices such ascamcorder 31, camera-phones 32, digital still camera 33 etc. The mediacan be stored in a private database 21 of the user computer 20 and/or beloaded to the interaction server 10.

If the user stores the media on the user computer 20, the contentanalysis engine 22 of the user computer 20 analyzes the media usingdatabase accesses to a database 23 of the user computer 20. The database 23 can store private data and private meta-data of the user.Another database 11 (also referred to as on-line database) can storedata and meta-data shared by multiple users. The other database 11 and acontent analysis server 12 belong to the interaction server 10.

The analysis results of the content analysis engine 22 or of the contentanalysis server 12 can be stored in either one of the databases 11 and23—based on, at least, a selection of a user

The user can directly upload media to the interaction server 10. In thiscase, the media is stored on the online database 11 and be analyzed bythe content analysis server 12. The resulting data and meta-data can bestored on the Online database 11. Another option for the user is to usea combination of the approaches above: Uploading to the Interactionserver, downloading and synchronizing to the user computer andprocessing in the Content Analysis Engine.

FIG. 2 illustrates an interaction between a interaction server 10, theuser computer 20, a mobile network 50 and the Internet 60 according toan embodiment of the invention.

The user can interact using a User Interface (UI) Application whichmight be a standalone application or a web application in a web browser.Using this UI the user can search, browse, produce and broadcast hispersonal media (stored on the user computer 30). The UI may get inputfrom the original user media (e.g., on ‘My Video/My Pictures or otheruser media locations) with the extracted data and meta-data from theprivate and online databases 11, 15, 21 and 23. For instance, even ifthe user computer 20 has no private database, the user can still searchand browse the online databases 11 and 13 using the UI. Using the MobileApplication UI 60 the user can search and browse the data on theinteraction server 10 (according to his user privacy settings) frommobile platform (e.g., Cellular phones, iPad). Users as well as Visitorscan view, browse and search media on the Interaction server using the‘Local Player’ (e.g., Flash Player embedded in HTML pages) which can beembedded in other web content.

Browsing

Browsing enables users to quickly find interesting information, when theusers cannot easily describe what they are seeking. For this mode ofassociative discovery, it should be easy to understand the content of avideo and to quickly navigate inside video and between semanticallyrelated video clips.

In order to support browsing the invention enables automaticallygeneration of a table of content, of intelligent preview and thumbnails,of links to “similar” video, content based fast-forwarding and spatialvideo browsing.

Table of content may be a table-of-visual content (optionallyhierarchical), which segments a video (or any other set of visualentities) to scenes with similar visual content. Note that these scenesusually cannot be separated by detecting different shots and they mightoverlap in time (e.g., the cameraman zooms in on a first context thenmoves on to a second context, then returns to the first context).

Intelligent preview and thumbnails may include a very short (e.g., 5-10seconds long) summary of the most representative portions of the video.This condensed summary enables the user to get a quick impression of thecontent in the video. It could comprise frames (storyboard), short clipsor a combination of both. Such short representation can be even used asan intelligent thumbnail that plays the video preview when the userselects it (e.g., mouse hovers over thumbnail).

Link to “similar” video—may include a list of related video and images,where relatedness is determined according to direct visual similarity aswell as semantic similarity of the visual content: similar persons,similar objects, similar place, similar event, similar scene, similartime. The link can either point to an entire clip or to a time frame init. Such links enable associative browsing when the user in not seekinga specific content.

Content-based fast forward. Viewing personal video may become a boringtask very quickly, as real-life activity tends to repeat itself.Content-based fast-forward enables the user to fast forward to the nextnovel activity (with different actions, behavior, etc'). This capabilityis executed either by adapting the speedup to the (automaticallydetermined) degree of interest or by jumping to the next interestingsegment in the video.

Spatial Video Browsing. In many video shots, the camera wanders aroundwhile scanning the area of interest. Spatial Browsing enables the userto freeze time and simulate spatial browsing with the camera. Namely, inresponse to a request from the user to move the camera (via keyboard,mouse or touch screen) the viewed image will change to an image with theproper camera point of view.

Searching

The Search engine enables the users to quickly retrieve informationaccording to a given criterion. Searching can be done using a visual ortextual query. In order to enable searching the method enables deep,frame-based indexing, automatic tagging and keywords and criterion basedsearch.

Deep, frame-based indexing—The method creates an index of objects,actions, faces, facial expressions, type of sound, places and people.Objects includes among many possible options pets, cars, computers,cellular phones, books, paintings, TV, tables, chairs etc. The indexingincludes the extraction of new entities, comparing them to knownentities (e.g., a known face) and keeping an index item for them. Theindex can be associated with a frame, a video segment or with the entirevideo clip.

Automatic Tagging and Keywords—The method clusters repeating entities(e.g., a repeating face) and generates a tag from it. A tag has a visualrepresentation (e.g., image of a face) and a textual tag (e.g., name ofa person). The user can name a visual tag. Each frame has a list of tagsand each video has a list of the most important (frequent) tags. Theuser can add his own tags to the automatically generated tags. When atag has a semantic meaning (e.g., ‘dog’ as opposed to ‘Rexy’) the methodrelates the semantic meaning of the tag to other synonym keywordsenabling easier textual search.

Criterion based Search—The user can search by a query combining freetext, visual and textual tags. The method finds the video or the imagesthat are most relevant to the query. For instance, the user can select apicture of a person's face, select the textual tag ‘living-room’ and addfree text ‘birthday party’ (which is used as a keyword).

Automatic Editing and Production—In order to support sharing andbroadcasting of personal video the raw video should be edited andproduced automatically (or with minimal user interaction). The methodmay enable at least one of the following: (a) Automatic Editing of Videoand Images; (b) Semi-Automatic Editing of Video and Images; (c)Automatic Video production of selected clips; (d) AutomaticInterpretation of user directives; (e) Manual Post Production; (f)Personalized Production; (g) Professional Production; (h) AutomaticMovie “Trailer”; (i) Automatic Content Suggestions; (j) Automatic Newsand Updates; (k) Automatic Group and Event Suggestions; (l)Graphics-Video interaction; (m) Return to original video; (n) Uploadingand Broadcasting: and (o) Documentary web-pages.

Automatic Editing of Video and Images—The method automatically selectsand edits clips and images from raw video and images input, in order tocreate a shorter video summary. The automatic editing relies on variousfactors for choosing the most important parts: Faces, knownpersons/objects, camera motion/zoom, video and image quality, actionsaliency, photo-artistic quality, type of voice/sound, facial expression(e.g., smile).

As a part of the editing process, the image quality is improved usingde-noising, video stabilization and super-resolution. The automaticediting can change the speed of a video (e.g., slow motion/fast motion)or even convert a video clip to an image if, for instance, the clip istoo short. Another case for converting video clip to image, is when thecamera pans and the automatic editing decides to create a mosaic imagefrom the clip.

The user can select a sound track to add to the edited video. Priormeta-data and analysis on the audio track might affect the automaticediting decisions (e.g., fast pace, short clips for high tempo audiotrack). The automatic editing is generating the selected clips (andimages) to fit a video length specified by the user (e.g., 45 seconds).

Semi-Automatic Editing of Video and Images—The user can modify theresulting automatic editing by the following operations:

-   -   Removing an unwanted clip    -   Adding a suggested clip (from an automatically prepared        candidate list)    -   Selecting one of more faces to be emphasized or excluded from        the edited video. This lists of faces is automatically extracted        from the video and can be displayed to the user using a        graphical user interface similar to the figure below.    -   Other types of object or tagged entities can be similarly        removed or emphasized (e.g. emphasizing a certain location).

FIG. 11 illustrates a process of editing a video entity.

Symbols representing media entity portions of interest 220, media entityportions that may be of interest 230 (but may have a lower importancelevel), features 240 (such as faces of persons) and feature attributes250 can be displayed to the user. The user can select which media entityportions to include in an edited media entity and can, additionally oralternatively, indicate an attribute such as an importance level offeatures. An attribute can reflect a preference of a user—forexample—whether the feature is important or not, a level of importanceof the feature, or any other attribute that may affect an editing thatis responsive to the attribute.

According to an embodiment of the invention an editing process caninclude one or more iterations. The user can be presented with mediaentity portions of interest, features, and even an edited media entityand receive feedback from the user (whether to alter the edited mediaentity, which features are more important or less important, addingmedia entity portions of interest, defining a level of interest thatshould allow an media entity of interest to be considered as a candidateto be included in an edited media entity, and the like.

These inputs are provided to any of the mentioned above blocks or systemthat may edit the edited media entity in response. The importance levelprovided by the user is taken into account during the editing—as imagesthat may include features that were requested by the user will me morelikely be included in the edited media entity.

Automatic Video production of selected clips—The selected clips andimages can be used in a straightforward manner to create a video clipsummary. However, the method can also provide a much more compellingautomatically produced video clip. The automatic production makes use ofa library of effects, transitions, graphic assets and sound tracks,which are determined according to the video and the extracted meta-data.For instance, an algorithm can choose to use a face-morphing transitioneffect between two clips, where the first clip ends in a face and thesecond clip starts in a different face. Another example is to use aneffect where the frame is moving in the direction of the camera motion.

Automatic Interpretation of user directives—The user can act as adirector during the filming of the video and perform various predefinedgestures, in order to guide the later automatic editing and productionstage. For instance, a user can indicate that he would like to create amosaic by passing a finger from one side of the camera to the other andthen panning slowly. Another example is that a user signals that he hasjust captured an important clip that should pop up in any editing by aspecial gesture (e.g. making ‘V’ with the fingers). In this manner, thesystem can identify user gestures and enables the user to act as thedirector of the automatic summarization in vivo.

Manual Post Production—The user can watch the resulting production andcan intervene to override automatic decision. For instance, the user canremove or add clips from a candidate list of clips using a simplecheckbox interface. In addition, the user can change the starting pointand end point of each selected clip. Moreover, user can change thetransitions if he likes, in a post production stage.

Personalized Production—besides manual post editing, the user can affectthe automatic production and editing stages using a search query, whichemphasizes the parts in the video, which are important to the user. Thequery can take the form of a full search query (text+tags+keywords). Forinstance, a query of the form ‘Danny jumping in the living room’ wouldput more emphasize in the editing and the production stages on partswhich fit the query. Another example is of a query which uses a visualtag describing a pet dog and a location tag with an image of the backyard. Another option for the user to affect the editing stage is bydirectly marking a sub-clip in the video which must appear in theproduction. Yet another example is that the user marks several people(resulting from Face Clustering and Recognition) and gets severalproductions, each production with the selected person highlighted in theresulting clip, suitable for sharing with that respective person.

Professional Production—The method allows an additional, professionalhuman editing and production. The method delivers the raw video, theextracted meta-data and the automatically produced video to professionalproducers (via internet or via a delivery service using DVDs etc.).After the professional editing, the user receives a final product (e.g.,produced DVD) via mail or delivery. Such a professional production cancomplement the automatic production when professional quality is needed(e.g., for souvenirs, presents). Alternatively, the method can exportthe automatic editing and the respective meta-data to common videoediting formats (e.g., Adobe Premiere, Apple Final Cut).

Automatic Movie “Trailer”—The method described above for editing andproduction of video can be used to create an automatic movie trailer forevery video in the user library. This is a produced version of the videopreview, which can be served as the default version for sharing a singlevideo. This “Trailer” can also be used as a short version for variouskinds of user generated content (even if not personal), for instance forautomatic “Trailers” of popular YouTube videos for users who prefer toview the highlight before viewing the entire video.

Automatic Content Suggestions—The method automatically suggests to theuser edited video clips which are suitable for sharing. For instance,after the video from a recent trip was loaded to the user computer, themethod automatically produces the relevant data and suggests it to theuser, who can decide to share the suggestion by a simple approval of thesuggestion.

Automatic News and Updates—The method uses the extracted meta-data toautomatically find shared video and images which might interest theuser. For instance, the method can suggest to the user to view a videoin one of his friend's shared content in which he participates. In thismanner, a user can be informed of visual information, which may be ofinterest to him, even if he did not upload the video by himself.

Automatic Group and Event Suggestions—The method uses the extractedmeta-data and discovered similarities between user data and shared datato propose formation of groups of people (e.g., close family, tripfriends) and event suggestions (e.g., trip, party, birthday). In thismanner, shared media entities, which can be clustered with other media,can be grouped in a semi-automatic manner (with user approval). Inaddition, the method can suggest producing personalized summaries ofevents—for instance, generating a different summary for each chosenparticipant in which this participant is highlighted in the generatedsynopsis. Such personalized summaries can encourage event and groupparticipants to add their own media from the event, remix the resultsand so on. This can promote the building a large media pool of an eventor a group.

Graphics-Video interaction—The method enables to add a layer ofgraphic-video interaction, based on the extracted meta-data. Forinstance, a conversation bubble can track a person's head or face.Another example is of a graphic sprite interacting with the video (e.g.,a fly added as a graphic layer to the video and which avoids a person ashe moves in the clip). This added layer can be disabled by the user.

Return to original video—The method enables the user to return to theoriginal video clip from any point in the produced video bydouble-clicking (or tapping in touch screen) the display in that point.

Uploading and Broadcasting—The method enables the user to upload theproduced video and related meta-data to a video storage site, whichenables to embed the video to be streamed via a video player (e.g.,Flash Player) in various internet locations including: email, socialnetworks, blog sites, home pages, content management systems, image andvideo sharing sites.

Documentary web-pages—The method enables the user to create documentaryweb pages, which are dedicated for a certain entity such as event,person, group and object. For example, creating a web page of a child,where video clips and images of the child are kept, documenting thechild at different stages of his life. Another example is a pagedocumenting a party where all participating users are invited to viewcurrent productions, upload their footage of the party, invite furtherparticipants and use all uploaded footage to create new productions (andso on). A different example is a web page documenting a user's trips inthe world. Yet another important example is a memorial page dedicated tothe memory of a deceased person. The system can automatically detect newvideos or images that are relevant to the documentary page, and add themto the page via approval of the user. This web page can be organized asan album or as a storyboard, and can be accompanied with annotations andtext that was inserted automatically (using the meta-data) or by theuser.

FIG. 3 illustrates a method 300 according to an embodiment of theinvention.

Method 300 may start by stage 302 or 304. These stages are followed by asequence of stages 310, 320, 330, 340, 350 and 360.

Stage 302 includes selecting, by a user, clips and images to be includedin the production, a time limit and an optional query for indicatingimportance for the editing stage.

Stage 304 includes selecting, by the content analysis server or contentanalysis engine, clips and images automatically to be used in a proposedproduction

Stage 310 includes completing, by the content analysis server or thecontent analysis engine, any unfinished analysis (if any) for therequested media

Stage 320 includes using the ImportanSee measure and other meta-dataproperties to automatically provide at least one video editing proposal

Stage 330 includes adding, automatically, production graphics to thevideo according to the meta-data. Optionally suggesting by theproduction graphics, an audio track to add to the production

Stage 340 includes presenting the results to the user. The results mayinclude clip selection, additional media clip/images proposals (whichare currently out of the production), and relevant graphical effects.Optionally also previewing by the user the current production.

Stage 350 includes adapting the selection: changing start/end points,selected clips, audio track etc.

Stage 360 includes saving video production compilation in meta-data DBand produce video after obtaining user approval.

The Media Predictability Framework

The long list of features above is very difficult to implement in an adhoc manner. Instead, the proposed method relies on a unified mediacontent analysis platform, which we denote as the media predictabilityframework. In this framework, we measure to what extent a query media(visual or audio) entity is predictable from other reference mediaentities and use it to derive meta-data on this query entity: Forinstance, if a query media is un-predictable given the reference media,we might say that this media entity is interesting or surprising. We canutilize this measurement, for example, to detect interesting parts in amovie by seeking for video segments that are unpredictable in thismanner from the rest of the video. In addition, we can use the mediapredictability framework to associate between related media entities.For example, we can associate a photo of a face with a specific personif this photo is highly predictable from other photos of that person.

In the sections below we first describe the theoretical foundations ofthe media predictability framework, then detail the implementation ofthe media analysis building blocks using this framework. Lastly, wedescribe how to implement the diverse features above, providing acomprehensive solution for personal video using the media analysisbuilding blocks.

A Non Parametric approach for determining Media Predictability

The predictability framework is a non-parametric probabilistic approachfor media analysis, which is used by our method as a unified frameworkfor all the basic building blocks that require high-level mediaanalysis: Recognition, Clustering, Classification, SalienSee Detection,etc'. We will first describe in detail the predictability framework andthen show how to derive from it the different building blocks.

Generally speaking, the predictability measure is defined as follows:Given a query media entity d and a reference media entity C (e.g.—portions of images, videos or audio) we say that d is predictable fromC if the likelihood P(d|C) is high, and un-predictable if it is low. Inthis section we describe how to actually compute this predictabilityscore in a unified manner, regardless of the application.

Descriptor Extraction

In this subsection we describe how to extract descriptors for a mediaentity.

A specific case of media descriptors is image descriptors. Each imagedescriptor describes a patch or region of interest or arbitrarily shapedregion in the image (this can also be the entire image). One of the mostinformative image descriptors is the Daisy descriptor (Fua 2008) whichcomputes a gradient image, and then, for each sample point, produces alog-polar sampling (of size 200) of the gradient image around this point(a detailed description is given in (Fua 2008)). Video descriptorsdescribe space-time regions (e.g. x-y-t cube in a video). Examples ofvideo descriptors include, raw space-time patches or concatenating Daisydescriptors applied on several consecutive frames (e.g. —3 frames,yielding a descriptor of length 200×3=600 around each sample point).However, there are many types of descriptors, known in the literature,that capture different aspects of the media, such as—simple imagepatches, shape descriptors (See for example (G. Mori, S. Belongie, andJ. Malik 2005)), color descriptors, motion descriptors, etc. Informationfrom different types of descriptors can be fused to produce betterpredictability estimation.

Similar to visual descriptors, audio can also be analyzed using audiodescriptors. Some audio descriptors that are popular in the literatureare MFCC, PLP, or the short-time spectrum. Audio descriptors can bespecialized for speech representation, music representation, or generalsound analysis. These descriptors can be computed, for example, usingopen source tools such as the CMU sphinx(http://cmusphinx.sourceforge.net/). Although each media has its ownvery different descriptor type, our predictability framework isapplicable to all descriptor and media types.

FIG. 4 illustrates a pre-processing block 40 according to an embodimentof the invention.

The pre-processing block 40 receives reference media entities 101 and aset of media data and outputs reference media descriptors 103 that canbe stored in a media descriptors database.

The pre-processing block 40 processes the reference media entities 101by a descriptor extractor 44 to provide a descriptor set of thereference media entities. The pro-processing block 40 generates (bydescription extractor 41 and representative extractor 42) a descriptorspace representatives of the set of media data 102. The descriptor setof the reference media entities and the descriptor space representativeare fed to a likelihood estimator 45 that outputs the reference mediadescriptors 103.

Descriptor Extraction: Given a reference set of media entities C, wefirst compute a set of descriptors over a set of sampling points. Thesampling points can be a uniform dense sampling of the media (forexample, a grid in an image) or only at points of interest (e.g.—corners in image). Let {f₁ ^(c), . . . , f_(K) ^(c)} denote the set ofdescriptors computed for the media reference C.

Descriptor-Space Representatives: Given a set of media entities (can bethe reference media itself), the descriptors for these entities areextracted. Next, the representative set is extracted from the fulldescriptor set in the following manner. A random sampling of thedescriptor can be used to generate representative, butvector-quantization might also be used (for example—using mean-shift ork-means quantization, etc').

Density Estimation: Given both the descriptor-space representatives {q₁,. . . , q_(L)}, and the descriptor set extracted from the referenceC—{f₁ ^(c), . . . , f_(K) ^(c)}, the next step is likelihood estimation.{f₁ ^(c), . . . , f_(K) ^(c)} is an empirical sampling from theunderlying probability distribution of the reference. In this step, weestimate the log likelihood log P (q_(i)) of each representative q_(i)in this empirical distribution. Several non-parametric probabilitydensity estimation methods exist in the literature. The Parzenestimation of the likelihood is given by:

${\hat{p}( { q_{i} \middle| f_{1}^{C} ,\ldots\mspace{14mu},f_{K}^{C}} )} = {\frac{1}{k}{\sum\limits_{j = 1}^{K}{K( {q_{i},f_{j}^{C}} )}}}$

where K(.) is the Parzen kernel function (which is a non-negativeoperator and integrates to 1;

A common kernel is the Gaussian kernel: (q_(i), f_(j)^(C))=exp(s∥q_(i)−f_(j) ^(C)∥²)) with s representing a fixed kernelwidth. The set of descriptor-representatives {q₁, . . . , q_(L)}together with their corresponding likelihoods {P(q₁), . . . , P(q_(L))}and the original descriptors {f₁ ^(c), . . . , f_(K) ^(c)} are used toconstruct the Media Descriptors Data-base, which is used in the queryblock.

FIG. 5 illustrates a query block 50 according to an embodiment of theinvention.

The query block 50 receives a query media entity (d) 104, referencemedia descriptors from reference descriptor database and outputs apredictability score P(d|C) 54. The query block 50 includes adescription extractor 51, a set (1 to K) of descriptor likelihoodestimators 52(1)-52(k) and a combination unit 53.

Descriptor Extraction 51: Given a query media entity d, we first computea set of descriptors {f₁ ^(d), . . . , f_(N) ^(d)} over a set ofsampling points (similar to the descriptor extraction step of thepre-processing block).

In addition, each descriptor is attached with a weight m_(i) of itssample point, which can be user defined. Commonly, we use uniformweights, but other weighting schemes can be used: for example, giving alarger weight to a region of interest (e.g. a ROI in an image whichgives a weight of 1 to all descriptors inside the ROI, and zerooutside).

Media likelihood Estimation 52(l)-52(K): For each descriptor f_(i) ^(d),the log-likelihood log P(f_(i) ^(d)|C) is estimated, where C is thereference media. The log-likelihood of each descriptor can be estimatedin the following way:log P(f _(i) ^(d) |C)=w ₁ log P(q ₁)+ . . . +w _(L) log P(q _(L)),(Σw_(k)=1)

Where P(q_(k)) are pre-computed values extracted from the referencemedia descriptor database, w_(k) are interpolation weights which aredetermined as a function of the distance of f_(i) ^(d) from q_(k). Thesimplest weighting scheme is linear, by setting w_(k)∝∥f_(i)^(d)−q_(k)∥⁻¹. This estimation can be approximated by taking only thefirst few nearest neighbors representatives, and setting w_(k) to zerofor the rest of the representatives.

More generally, the log-likelihood log P(f_(i) ^(d)|C) can be estimatedusing a non-linear function of the representative log-likelihood valuesand the distances from them:log P(f _(i) ^(d) |C)=F({log P(q ₁),log P(q _(L)),∥f_(i) ^(d) −q ₁ ∥, .. . ,∥f _(i) ^(d) −q _(L)∥})

Combination: All the likelihoods of the different descriptors arecombined to a predictability score of the entire query media entity d.The simplest combination is a weighed sum of the log-likelihoodestimations:PredictabilityScore(d∥C)=Σm _(i)·log P(f _(i) ^(d) ∥C).

Where m_(i) are the sample point weights mentioned above. If we havemultiple types of descriptors (referred below as aspects), {f₁₁ ^(d), .. . , f_(N1) ^(d)}, . . . , {f_(1R) ^(d), . . . , f_(NR) ^(d)} (I.e. —Rdifferent descriptor types or R aspects), the combined score becomes:PredictabilityScore(d|C)=Σ_(r=1) ^(R)α_(r)Σ_(i=1) ^(N) m _(i)·log P(f_(ir) ^(d) |C)

Where α_(r) are weights of each aspect (they can be determined manuallyor automatically from a training set).

More generally, dependencies between the different descriptor types canbe taken into account by setting:F _(Q)=[(Σ_(i=1) ^(N) m _(i)·log P(f _(i1) ^(d) |C))^(0.5), . . .,(Σ_(i=1) ^(N) m _(i) ·F(f _(iR) ^(d) |C))^(0.5)]

And:PredictabilityScore(d|C)=F _(Q) ^(T) *A*F _(Q)

Where A encapsulates the dependencies between the different descriptortypes (a diagonal matrix A will yield the previous formula, while takingthe covariance matrix estimated empirically will yield the generalformula).

Empirical Predictability Improvement.

The predictability score can be further improved using empiricalpost-processing.

Specifically, given a single media entity d, sometimes thepredictability scores for several media referencesPredictabilityScore(d|C₁), . . . , PredictabilityScore(d|C_(S)) aredependent.

As a result, comparing between different reference media sets can beimproved by empirically estimating the distribution of thepredictability score over a “training” set. This training set aims torepresent the set of queries, so it is best (if possible) to draw itrandomly from the query set. Note that the distribution that we aretrying to estimate now is simply the distribution of the predictabilityscores of a media entity given a set of references C₁, . . . C_(S) (notethat this generated a new “feature” vector of dimension S forrepresenting the query media). A straightforward approach is to use thenon-parametric Parzen estimation, which has been described earlier, orrecursively using our non-parametric likelihood estimation.

Media Analysis Building Blocks

In this section we describe how to derive each building block using themedia predictability framework. The text below refers to the case ofusing a single aspect but the same approach holds for multiple aspects.

FIG. 6 illustrates a similarity block 60 according to an embodiment ofthe invention.

The similarity block 60 (also referred to as a similarity buildingblock) is used to quantify the similarity between two media entitiesM1,M2. To do so, we use each media entity twice: once as a reference,and once as a query.

Referring to FIG. 6, the similarity block 60 receives a first mediaentity 111 and a second media entity 112. The first media entity isprovided to a pre-processing block 61 (when used as a reference) thatextracts first media entity descriptor space representatives that arefed (in addition to the second media entity) to a query block 50. Thequery block 50 outputs a predictability score of the second media entitygiven the first media entity.

The second media entity is provided to a pre-processing block 61 (whenused as a reference) that extracts second media entity descriptor spacerepresentatives that are fed (in addition to the first media entity) toanother query block 50. The other query block 50 outputs apredictability score of the first media entity given the second mediaentity.

Both predictability scores are fed to a unification unit 53 that outputssimilarity(M1, M2) 65.

In more details:

A descriptor database is constructed from each media entity (using thepre-processing block—as was shown in the pre-processing section of thepredictability framework).

The predictability PredictabilityScore(M₁|M₂) of media entity M₁ giventhe media entity M₂ as a reference is computed using the query block (asshown in the query section of the predictability framework).

Similarly, the predictability PredictabilityScore(M₂|M₁) of media entityM₂ given the media entity M₁ as a reference is computed.

The two predictability scores are combined to produce a singlesimilarity measure. As a combination function, one can use any bimodaloperator according to the specific application, such as the ‘average’ orthe ‘max’ operators.

The “Classification” Building Block

FIG. 7 illustrates a classification building block 70 according to anembodiment of the invention. The classification building block is alsoreferred to as classification block.

The classification building block is used to classify a media entityinto one of several classes. To do so, we collect a set of mediaentities that relates to each class, construct a media descriptor DBfrom each reference class, and compare the query media to all of themusing the query building block.

The classification block 70 receives reference media entities of eachclass out of multiple media classes—C1 120(1)-120(N).

A query media entity d 104 and reference media entities of each classare fed to N query blocks 50—each query block receives the query mediaentity d and one of the reference media entities of a class—separatequery blocks receive reference media entities of different classes. Eachquery block 50 outputs a predictability score of the query media entitygiven the media entity class. A classification decision block 72classifies the query media entity to one or these classes base don thepredictability scores.

In more details:

For each class C_(i), an example set of media entities relating to thisclass is selected.

For each set of entities, a descriptor database DB_(i) is constructedusing the pre-processing block—as was shown in the pre-processingsection of the predictability framework.

The predictability PredictabilityScore(d|C_(i)) of the query mediaentity d given each class is estimated using the query block (as shownin the query section of the predictability framework).

Finally, the predictability scores are entered into the classificationdecision block, which outputs the classification of d (Note that theclassification doesn't necessarily have to be a hard decision on asingle class, but it can be the posterior probability of d to belong toeach class). The simplest decision rule is setting the classification ofd to be the class C for which the predictability score of d given C isthe highest. But other decision rules are also possible—for example,computing posterior probabilities (given the prior probabilities of eachclass). In addition, the distribution of the predictability scores givenall (or subset) of the classes can be estimated using a “training” set.(A simple way to do it is using the non-parametric Parzen estimation, asdescribed earlier). With this empirical distribution estimation, theprobability of classifying d with each class can now be determineddirectly from the distribution, providing “Empirically Corrected”probabilities.

The “Detection” Building Block

The classification block can operate as a detection block. Assuming thata certain feature is being searched in a query media stream. Onereference media entity class is selected as including the feature asanother reference media entity class is selected as not including thefeature. The query media entity and these two media entity classes arefed to the classification block that classifies the query media entityas being included in one of these media classes-a s including thefeature or not including the feature. It is noted that more than twomedia classes can be provided and may include different associationswith the feature (not just a binary relationship of including or notincluding the feature).

FIG. 10 illustrates a decision block according to an embodiment of theinvention. A set of media entities 160 that is pre-filtered 99 toprovide a set of candidates for searching the feature within. The set ofcandidates and two classes of reference examples 162 and 164 areprovided to a classification block 98 that decides whether the featureexists in the candidates. The output is a list of detections 97 thatindicates in which candidates the feature appears.

The detection building block is used to detect some pre-defined class(for example—face detection, or a detection of some specific person)inside a set of media entities. The detection building block is actuallya special case of the classification building block, in which the tworeference classes are the “Class” and the “Non-Class” (forexample—“Face”-“Non Face”, “Speech”-“Non-Speech”), and the set ofqueries is all the sub-segments of the media for which we would like toapply the detection—for example, a set of sub-windows in a image.

Since the classification process usually takes too much time to beapplied on all sub-segments, a pre-filtering can be applied, choosingonly a subset of the segments. For example, the cascade based Viola &Jones method is widely used for object (e.g., face) detection,outputting a set of rectangles for which a face was detected. Yet, italso outputs a large set of erroneous detections, which can be furthereliminated by the “Class”-“Non Class” detection block describe herein.See

for a schematic description of the detection building block.

The “Clustering” building block

The clustering building block is used to cluster a set of media entitiesinto groups. This building block is using the similarity building blockdescribed above to compute a similarity measure between pairs of mediaentities, and then use standard clustering methods to cluster theaffinity matrix.

FIG. 8 illustrates a clustering block 80 according to an embodiment ofthe invention.

The clustering block 80 includes multiple similarity blocks 60 that arefed with different media entities. During each iteration the clusteringblocks output a similarity score between two media entities. Thesesimilarity scores can be arranged to form a similarity/affinity matrix(or any other data structure) that is fed to a clustering algorithm 81that clusters the media entities based on the similarityscores—clustering M1, . . . , MN 85.

In more details:

For each pair of media entities M_(i) and M_(j), the similarity betweenthem is computed using the similarity building block (described above).

A similarity matrix A_(ij) is computed by A_(ij)=similarity (M_(i),M_(j)). This similarity matrix forms an Affinity matrix which is acommon input for many clustering algorithms.

Finally, doing clustering from a Similarity or an Affinity matrix iswell known in the art (For example, Agglomerative hierarchicalclustering, spectral clustering (Andrew Y. Ng and Michael I. Jordan andYair Weiss 2001) or simply merging all pairs for which similarity(M_(i),M_(j))>Threshold.

The “Saliensee” Building Block

FIG. 9 illustrates a SalienSee block 90 according to an embodiment ofthe invention.

The SalienSee block tries to predict a portion of a media entity (It)based on previous media entity portions (I1 . . . It−1) that precede it.

An input media entity 130 that includes multiple media entity portionsis fed to the SalienSee block 90 one media entity portion after theother so that the media entity portions can be evaluated in an iterativemanner—one after the other.

At point of time t a media entity portion (It) based on previous mediaentity portions (I1 . . . It−1) that precede it.

Query block 50 receives (as a query media entity) the media entityportion It and receives (as reference descriptor space representative)descriptors space representatives of the previous media entity portions.

The query block 50 calculates a predictability score that may beregarded as a saltiness score 95, The media entity portions are also fedto a database 92. The content of the database are processed bypre-processing block 40.

The proposed method uses a new measure called “SalienSee”. It measuresthe extent by which a point in time in the media is salient in themedia. This can also indicate that this point in time is “surprising”,“unusual” or “interesting”. We say that a media entity has highSalienSee if it cannot be predicted from some reference set of mediaentities. Let d be some query media entity, and let C denote thereference set of media entities. We define the SalienSee of d withrespect to C as the negative log predictability of d given C (i.e.SalienSee(d|C)=−log PredictabilityScore(d|C)). Using this notation, wecan say an event is unusual if its SalienSee measure given other eventsis high. For instance, the SalienSee measure can capture the moments invideo in which the activity becomes boring (which is very common in apersonal video)—for example, when someone starts jumping it might beinteresting, but the next jumps are getting more and more boring as theyare already very predictable from the past. Formally, let I (t₁, t₂)denote the time segment t₁<t<t₂ of the video clip d. We say that thevideo d (t, t+δt) is ‘boring’ if its SalienSee measure with respect tothe past is small, i.e, if SalienSee(d(t,t+δt)|d(t−T,t))<S, where T, δtare some periods of time (e.g. —T is a minute, δt is a second.

Implementing the Personal Video Features Above Using the Building Blocks

As shown in the previous sub-section, all the basic building blocks thatare used by the proposed method can be directly implemented using themedia predictability framework. Next, we show how these building blocks(e.g., Recognition, Clustering) can be used to realize the long list offeatures, presented above, in order to enable comprehensive solution forsearching, browsing, editing and production of personal video.

Tagging: Automatic tagging of media entities is achieved by applying theDetection/Recognition building block several times. Some tags areextracted by solving a detection problem. For instance adding a tag“face” whenever the face detector detected a face in a video clip, or atag “applause” when a sound of clapping hands is detected. Other typesof tags are extracted by solving a recognition (or classification)problem. For instance, a specific person-tag is added whenever theface-recognition module classifies a detected face as a specific,previously known face. Another example is classifying a scene to be“living-room scene” out of several possibilities of pre-defined scenelocation types. The combination of many detection and recognitionmodules can produce a rich and deep tagging of the media assets, whichis valuable for many of the features described below.

The method utilizes at least some of the following tagging: face poses(“frontal”, “profile” etc.), specific persons, facial expressions(“smile”, “frown” etc.), scene-types (“living-room”, “backyard”,“seaside” etc.), behavior type (“running”, “jumping”, “dancing”,“clapping-hands”etc.), speech detection, soundtrack segment beatclassification (e.g. “fast-beat”, “medium-beat”, “slow beat”), voiceclassification (“speech”, “shout”, “giggle”, etc.). Note that the MediaPredictability Framework enables a single unified method to handlerecognition and detection problems from completely different domains(from behavior recognition to audio classification), simply by supplyingexamples from the recognized classes (whether video, image or audioexamples).

ImportanSee: our “ImportanSee” measure is used to describe theimportance or the amount of interest of a video clip for someapplication—for example, in a video summary we can display only theimportant parts while omitting the non important ones. In principle,this measure is subjective, and cannot be determined automatically.However, in many cases it can be estimated with no user interventionusing attributes such as the attributes listed below:

SalienSee—Very low saliency clips are usually boring and not important.Therefore, we can attribute low importanSee to those clips.

Camera Motion: Camera motion is an important source of information onthe intent of the cameraman. A panning of the camera usually indicatesthat the photographer is either scanning the scene (to get a panorama ofthe view), or just changing the focus of attention. Video segments thatrelates to the second option (a wandering camera) can be assigned with alow ImportanSee. A case where the camera is very shaking and notstabilized can also reduce the overall ImportanSee. The camera motioncan be estimated using various common methods (e.g. (J. R. Bergen, P.Anandan, K. J. Hanna, and R. Hingorani 1992)).

Camera Zoom: A Camera zoom-in is usually a good indication for highimportance (i.e., resulting in high ImportanSee). In many cases, thephotographer zooms in on some object of interest to get a close-up viewof the subject (or event).

Face close-up: Images or video clips in which faces appear in the sceneare usually important. Specifically, a close-up on a face (in a frontalview) will usually indicate a clear intention of the photographer tocapture the person (or persons) being photographed, and can serve as astrong cue for high importanSee.

Speech: Speech detection and recognition can help detecting interestingperiods in the video. Moreover, laughter (general, or of a child)increases the ImportanSee measure of the corresponding video segment. Anexcited voice may also be used as a cue for importanSee.

Facial expressions: Facial expressions are a good cue for highImportanSee. For instance, moments when a person smiles or a childfrowns or cries indicates a high ImportanSee.

Given a visual entity d (for example, a video segment), the attributesabove can be used to compute intermediate importance scores s₁, . . .s_(l) (in our implementation, these scores can be negative. Such scorescan be obtained by using direct measurements (e.g, SalienSee measure ofa clip), or by some binary predicate using the extracted meta-data(e.g., s=1 if clip includes a ‘large face closeup’ tag and s=0otherwise). The final ImportanSee measure is given as a weighted sum ofall attribute scores. I.e., ImportanSee(d)=max (Σ_(i)α_(i)s_(i), 0),where α_(i) is the relative weights of each attribute.

Table of content: Table of (visual) content is a hierarchicalsegmentation of visual entities (video or set of videos and images).This feature can be implemented as a clustering of the various scenes ina video. For instance, by sampling short video chunks (e.g., 1 second ofvideo every 5 seconds of video) and clustering these media chunks (usingthe clustering building block) will produce a flat or hierarchical tableof contents of the video. In addition to this segmentation, each segmentis attached with either a textual or visual short description (forexample, a representative frame or a short clip). This representativecan be selected randomly, or according to its ImportanSee measure.

Intelligent preview and thumbnails: This is a very short (e.g., 5-10seconds long) summary of the most representative and important portionsof the video. This feature can be implemented by simply selecting thetime segments of the video with the maximal ImportanSee.

Video links and Associative browsing: This feature facilitates video andimage links, which are based on audio-visual and semantic similarity.This feature can be implemented as a combination of using the Taggingfeature and the similarity building block: The similarity building blockis used to quantify the direct audio-visual similarity between imagesand video. The Tagging feature is used to quantify the semanticassociation between media entities—for instance, two videos of birthdayparties, two videos of dogs etc. To quantify the semantic similarity,various simple distances can be used between the tag lists of each mediaentity, such as the number of mutual tags or a weighted sum of themutual tags, which emphasizes some tags over others. To quantify theoverall similarity a (weighted) sum of the semantic and audio-visualsimilarity can be used to combine the different similarity measures.Links between media entities can be formed for pairs of entities withhigh enough overall similarity.

Content-based fast forward: In Content-based fast-forward, interestingparts are displayed in a normal speed (or with a small speed-up), whileless interesting parts are skipped (or displayed very fast). This can bedone automatically using the ImportanSee measure: The speed-up of eachvideo segment d is determined as a function of its ImportanSee, I.e.speedup(d)=F(ImportanSee(d)). Two simple examples for F are F(x)=1/x andthe threshold function

${F(x)} = \{ \begin{matrix}1 & {{F(x)} > S} \\\infty & {{F(x)} \leq S}\end{matrix} $(which is equivalent to selecting the important video segments).

Automatic Video Editing & Synopsis: The main challenge in automaticvideo editing is to automatically select the most important sub-clips inthe video, which best represent the content of the original video. Thisselection is an essential stage for most of the features that relates toautomatic video editing: creating a video synopsis (or Movie “Trailer”),video production, intelligent thumbnails, etc'. This task is best servedby the ImportanSee building block (describe above)—to determine theimportance of each sub-clip in the video, and promoting the selection ofthe most important ones to be used in the edited video. Using the factthat we can compute the ImportanSee measure on any video sub-clip wedefine a video editing score for a video editing selection of clips c₁,. . . , c_(n) from a video v:score(c₁, . . . , c_(n))=Σ_(i)ImportanSee(c_(i)).

Thus we can pose the problem of automatic video editing as anoptimization of the editing score above given some constraints (e.g.,such that the total length of all selected sub-clips is not longer thanone-minute). This is a highly non-continuous function and isbest-optimized using stochastic optimization techniques (e.g., SimulatedAnnealing, Genetic Algorithms) where the score function is used toevaluate the quality of a selection and random selection and mutation(e.g., slightly changing clip starting and ending points) enablesdiscovery of the problem-space during the optimization process.

System

FIG. 12 illustrates a system and its environment according to anembodiment of the invention. The system implements any of the methodsdescribed above to provide a comprehensive solution for browsing,searching and sharing of personal video.

The system has various components which reside on several sites. Therelated sites and the components on them are described next.

User Computer 20—The user computer(Desktop, Laptop, Tablet,Media-Center, Pocket PC, Smartphone etc.) may include two databases 21and 23, content analysis engine 22 and user interface application 24.

The user computer can store a large amount of visual data in generallocations such as ‘My Video’ and ‘My Pictures’ directories in MicrosoftWindows Operation Systems. Most of the data in these locations is rawdata and yet personal.

The content analysis engine 22 may process runs in the background(optionally only during the computer idle time) or upon user request. Itanalyzes the user's visual data (videos and pictures), and extractsmeta-data using a work queue.

The work queue is filled by the content analysis engine 22 as well as bythe user selection (a user can insert any video or image to the top ofthe queue).

While the original video and images of the user may remain intact, thecontent analysis engine 22 may use the private Meta-Data DB 23 to storethe extracted meta-data and reuses this meta-data for its own analysis(e.g., extracted visual tags are stored there for future automatictagging).

In a difference embodiment the content analysis engine 22 is not asoftware installed on the user computer 20, but rather an internetbrowser plug-in or a software component (e.g., ActiveX) which enablesthe user to apply the content analysis engine 22 to run without fullsoftware installation (but a plug-in installation). In anotherembodiment of this system, there is not content analysis engine on the‘User Computer’. Instead, the user can make use of content analysisserver software (12) as a service which resides on the interactionserver 10.

The user interface application 24 lets the user apply a sub-set of themethod capabilities discussed above, thus enabling browsing, searchingand sharing of personal video. The sub-set depends on the type ofclient, license and computer. In one embodiment, this is a standaloneclient installed on the user computer. In another embodiment, this is aweb application which uses an internet browser for running the userinterface, which enables running it from any internet browser, withoutinstalling software.

Interaction Server

The interaction server 10 hosts several servers which enable users toshare personal video and images and broadcast them on various internetlocations by embedding them. The ‘User Profile’ 18 contains variousinformation about the user such as its personal details, a list ofaccounts in various internet services, a list of friend and familymembers and usage statistics. The ‘Public Data+Meta-Data DB’ 17 containsdata that the user selected to share from the ‘User Computer’: relevantmeta-data and also video clips, images, etc. Sharing can be limited tovarious groups—family, friends, everyone etc. The database is alsoresponsible for initiating synchronization with connected ‘UserComputers’ and mobile appliances. The ‘Content Analysis Server’ 12 is apowerful version of the content analysis engine on the user computer 20which enables to process a large amount of visual data being uploaded tothe site. This enables the user to process video even from a computerthat does not have the content analysis engine installed (i.e.,SaaS—Software as a Service).

The ‘Video Platform Server’ 19 performs the actual streaming andinteraction with users and visitors that view video and images stored onthe ‘Interaction server’. It contains the actual ‘Streaming’ module 194which is responsible for the actual delivery of the video on time andwith the right quality. The ‘Interaction’ module 192 is responsible forinterpreting the user requests (e.g., press on a table of contentselement) and communicate it with the ‘Streaming’ server or the ‘LocalPlayer’. The ‘Analytics’ module 193 is responsible for recording userbehavior and response for each video and advertise that was displayed onit (e.g., number of times a video was watched, number of skips, numberof times an ad was watched till its end). The ‘Ad-Logic’ 191 usesinformation from the ‘Analytics’ module to choose the best strategy toselect an ad for a specific video and user and how to display it. Thisinformation is synchronized in real-time with the ‘Local Player’. The‘Ad-Logic’ module can instruct the ‘Local Player’ to display an ad invarious forms, including: pre-roll, post-roll, banners, floating ads,textual ads, bubble ads, ads embedded as visual objects using theextracted video meta-data (e.g., adding a Coca-Cola bottle on a table).

Internet Locations

Users and visitors can view video and images which users decided toshare on various ‘Internet Locations’ 40 that may include socialnetworks, email services, blogs, MySpace, Gmail, Drupel, Facebook andthe like. The actual viewing of video is performed by an embedded playerwhich can be based on various platforms such as Adobe Flash, MicrosoftSilverlight, HTMLS etc. The player can be embedded either directly orusing a local application (e.g., Facebook application) in variousinternet locations including: Social Networks (e.g., Facebook, Myspace),Email messages, Homepages, Sharing-Sites (e.g, Flickr, Picasa), Bloggingsites and platforms (e.g., Wordpress, Blogger) and Content ManagementSystems (e.g., Drupal, Wikimedia). Alternatively to embedding a ‘LocalPlayer’ the user can user an internet link to a dedicated video page onthe ‘Interaction server’.

Mobile Networks

Users can view and synchronize video via mobile appliances (e.g., cellphones) using the cellular networks 50 or internet networks 40. In casesthat the mobile appliance is computationally strong enough (e.g.,Pocket-PC, Smartphone) it can be regarded as a ‘User Computer’. In othercases it can use a ‘Mobile Application’ which enables to view media fromthe ‘Interaction server’ as well as uploading raw media from the mobileappliance. In this manner the ‘Mobile Application’ can use the ‘ContentAnalysis Server’ in the ‘Interaction server’ to produce and share videoeven for appliances with low computational powers. Moreover, the‘Interaction server’ can automatically synchronize uploaded content withother connected ‘User Computers’.

Movie Production

Users can select to send automatically produced media for further,professional production by human experts. The system proceeds by sendingthe relevant raw video, the extracted meta-data and the automaticallyproduced video to a professional producer 70 (via internet or via adelivery service using DVDs etc.). After the professional editing isfinished, the user receives a final product (e.g., produced DVD) viamail or delivery.

Other Electronic Appliances

In other embodiments, the system is implemented on ‘Other ElectronicAppliances’ with do not utilize general CPUs or without enoughcomputational power. In these cases, parts of the software modulesdescribed in user computer are implemented in embedded form (ASIC, FPGA,DSP etc.).

FIG. 13 illustrates method 1300 according to an embodiment of theinvention. Method 1300 is for determining a predictability of a mediaentity portion.

Method 1300 starts by stage 1310 of receiving or generating (a)reference media descriptors, and (b) probability estimations ofdescriptor space representatives given the reference media descriptors;wherein the descriptor space representatives are representative of a setof media entities.

Stage 1310 is followed by stage 1320 of calculating a predictabilityscore of the media entity portion based on at least (a) the probabilityestimations of the descriptor space representatives given the referencemedia descriptors, and (b) relationships between the media entityportion descriptors and the descriptor space representatives.

Stage 1320 may be followed by stage 1330 of responding to thepredictability score.

Stages 1310-1330 can be repeated multiple times on multiple media entityportions.

Stage 1320 may include at least one of the following: (a) calculatingdistances between descriptors of the media entity and the descriptorspace representatives; (b) calculating a weighted sum of probabilityestimations of the descriptor space representatives, wherein weightsapplied for the weighted sum are determined according to distancesbetween descriptors of the media entity portion and descriptor spacerepresentatives; (c) generating the probability estimations given thereference media descriptors; wherein the generating comprisescalculating, for each descriptor space representative, a Parzenestimation of a probability of the descriptor space representative giventhe reference media descriptors.

According to an embodiment of the invention method 1300 may be appliedon different portions of a media entity in order to locate mediaportions of interest. Thus, stage 1320 may include calculating thepredictability of the media entity portion based on reference mediadescriptors that represent media entity portions that precede the mediaentity portion and belong to a same media entity as the media entityportion. Repeating stage 1310 and 1320 on multiple portions of the mediaentity can result in calculating the predictability of multiple mediaentity portions of the media entity and detecting media entity portionsof interest. Stage 1330 may include generating a representation of themedia entity from the media entity portions of interest.

According to an embodiment of the importance of a media entity portioncan be determined based on additional factors. Thus, stage 1320 can beaugmented to include defining a media entity portion as a media entityportion of interest based on the predictability of the media entityportion and on at least one out of a detection of a camera motion, adetection of a camera zoom or a detection of a face close-up.

FIG. 14 illustrates method 1400 according to an embodiment of theinvention. Method 1400 is for evaluating a relationship between a firstmedia entity and a second media entity.

Method 1400 starts by stage 1410 of determining a predictability of thefirst media entity given the second media entity based on (a)probability estimations of descriptor space representatives given secondmedia entity descriptors, wherein the descriptor space representativesare representative of a set of media entities and (b) relationshipsbetween second media entity descriptors and descriptors of the firstmedia entity.

Stage 1410 is followed by stage 1420 of determining a predictability ofthe second media entity given the first media entity based on (a)probability estimations of descriptor space representatives given firstmedia entity descriptors, and (b) the relationships between first mediaentity descriptors and descriptors of the second media entity.

Stage 1420 is followed by stage 1430 of evaluating a similarity valuebetween the first media entity and the second media entity based on thepredictability of the first media entity given the second media entityand the predictability of the second media entity given the first mediaentity.

Stage 1400 may be repeated multiple times, on multiple media entityportions. For example, it may include evaluating the relationshipsbetween multiple first media entities and multiple second media entitiesbased on a predictability of each first media entity given the multiplesecond media entities and a predictability of each second media entitygiven the first media entity.

Method 1400 can be used for clustering—by evaluating the sumilatiryvalue of a media entity to a cluster of media entities. Thus, method1400 can include clustering first and second media entities based on therelationships between the multiple first media entities and the multiplesecond media entities.

FIG. 15 illustrates method 1500 according to an embodiment of theinvention. Method 1500 is for classifying media entities.

Method 1500 starts by stage 1510 of receiving or generating (a) mediaclass descriptors for each media entity class out of a set of mediaentity classes, and (b) probability estimations of descriptor spacerepresentatives given each of the media entity classes; wherein thedescriptor space representatives are representative of a set of mediaentities.

Stage 1510 is followed by stage 1520 of calculating, for each pair ofmedia entity and media class, a predictability score based on (a) theprobability estimations of the descriptor space representatives giventhe media class descriptors of the media class, and (b) relationshipsbetween the media class descriptors and the descriptor spacerepresentatives descriptors of the media entity.

Stage 1520 is followed by stage 1530 of classifying each media entitybased on predictability scores of the media entity and each media class.

FIG. 16 illustrates method 1600 according to an embodiment of theinvention. Method 1600 is for searching for a feature in a media entity.

Method 1600 starts by stage 1610 of receiving or generating first mediaclass descriptors and second media class descriptors; wherein the firstmedia class descriptors represent a first media class of media entitiesthat comprises a first media feature; wherein the second media classdescriptors represent a second media class of media entities that doesnot comprise the first media feature.

Stage 1610 is followed by stage 1620 of calculating a predictabilityscore given a first media class based on (a) probability estimations ofdescriptor space representatives given the first media classdescriptors, and (b) relationships between the first media classdescriptors and descriptors of the media entity.

Stage 1620 is followed by stage 1630 of calculating a second media classpredictability score based on (a) probability estimations of descriptorspace representatives given the second media class descriptors, and (b)relationships between the second media class descriptors and descriptorsof the media entity.

Stage 1630 is followed by stage 1640 of determining whether the mediaentity comprises the feature based on the first media classpredictability score and the second media class predictability score.

Stage 1640 can be followed by stage 1650 of responding to thedetermination. For example, stage 1650 may include detecting mediaentities of interest in response to a detection of the feature.

Stage 1600 can be repeated in order to detect a feature in multiplemedia entities by repeating, for each media entity stages 1610-1650.

The feature can be a face but this is not necessarily so.

FIG. 17 illustrates method 1700 according to an embodiment of theinvention. Method 1700 is for processing media streams.

Method 1700 starts by stage 1710 of applying probabilisticnon-parametric process on the media stream to locate media portions ofinterest. Non-limiting examples of such probabilistic non-parametricprocess are provided in the specification.

A non-parametric probability estimation is an estimation that does notrely on data relating to predefined (or known in advance) probabilitydistribution, but derive probability estimations directly from the(sample) data.

Stage 1710 may include detecting media portions of interest in responseto at least one additional parameter out of: (a) a detection of a changeof focal length of a camera that acquires the media; (b) a detection ofa motion of the camera; (c) a detection of a face; (d) a detection ofpredefined sounds; (e) a detection of laughter; (f) a detection ofpredefined facial expressions; (g) a detection of an excited voice, and(h) detection of predefined behavior

Stage 1710 is followed by stage 1720 of generating metadata indicativeof the media portions of interest.

Stage 1720 may include adding tags to the media portions of interest.

Stage 1720 is followed by stage 1730 of responding to the metadata.

Stage 1730 may include at least one of the following: (a) generating arepresentation of the media stream from the media portions of interest;(b) generating a trick play media stream that comprises the mediaportions of interest; (c) finding media portions of interest that aresimilar to each other; (d) tagging media portions of interest that aresimilar to each other; and (e) editing the media stream based on themedia portions of interest.

Disclosed are solutions that extract visual tags using a non-parametricapproach, and does content moderation based on these visual tags. Thevisual information can be incorporated, according to various embodimentsof the invention, with additional information (text, links) to improveresults.

Provided are methods and systems to the problem of visual contentmoderation using computerized image and video content analysis.

The raw data is first being visually analyzed, providing a visualmeta-data. The visual meta-data can include, for example, the existenceof a face in the scene. Such a tagging can be done using the “DetectionBlock” (FIG. 10). Another example for a visual meta-data is thelikelihood of an image or a video clip to belong to some visual class,such as being an indoor or outdoor scene. Such a tagging can be doneusing the “Classification Block” (FIG. 7). More examples for visual tagsthat can be detected in an image or video are: face posses, body, upperbody, position (sitting, standing, etc'), nude person, dressedperon,patially-dressed person, person, man/woman, body parts, sexualactivity (for video), moving body (for video), moving part (for video),general movement.

The visual meta-data is used to attach semantic tags to the raw data,such as “Nudity”, “Porn”, “Scenery”, etc.

According to an embodiment of the invention, semantic tags may bedetermined from visual meta-data by using a decision tree. For example,saying that an image has “Nudity” in it if [,(naked−person)>p₁] (whereinp indicates probability and p₁ is a predetermined threshold) or sayingthat an image is “Scenery” in high probability if[p(outdoor)<p₂&p(people)<p₃]. Alternative ways to extract semantic tagsand methods to computerize this stage are described in the nextsections.

The semantic tags, together with user credentials, are being used as aninput for the content moderation stage. This stage determines thesuitability of the input raw data to various destination populations.For example, ‘Nudity’ might be fine for Teenagers, but inappropriate forchildren. ‘Porn’ may be appropriate only for adults (or for no one atsome populations or setting). Optionally, this stage can involve userintervention. For example—in a video was attached with a “Porn” tag inlow probability, we might want to let the video be moderated by a humanobserver. Finally, the content moderation is followed by anaction/decision. Common actions are: Filtering (not displaying theimage/video or blocking the entire cite in some cases), Sending aMassage' etc.

A method for operating based on content analysis can include receivingraw data by a visual analysis module, performing visual analysis,applying (on the outcome of the visual analysis) semantic tagging,performing (on the outcome of semantic tagging) moderation and finallyperforming a decision based on the outcome of the moderation and usercredentials.

Visual Analysis

The visual analysis is the stage in which the raw data is being visuallyanalyzed, and a visual meta-data is extracted. The meta-data can includemany visual classes, which can be either mutually exclusive or overlap.For example, the visual classes “Indoor Scene” and “Outdoor Scene” aremutually exclusive, while the visual class “Person” has overlap withboth. The visual classes can also be hierarchical—for example, “SexualActivity” can be divided into multiple poses, and a “Person” can bedivided into “Female” and “Male” sub-classes (which in turn can also bedivided into different poses such as “Standing”, “Sitting”, “UpperBody”, etc.).

For each such visual class, the meta-data can include a type (e.g. classname), a detection score (or probability), and optionally a ROI (forexample—bounding box where a face/person was detected, or duration inthe video in which nudity has been detected).

Given a new visual entity, the detection of visual classes relies mainlyon the “predictability framework” which is described previously in thespecification.

From Predictability to Meta-Data

As described before, the visual analysis stage extracts meta-data fromthe raw data. This meta-data consists of various visual classes (tags)such as, by way of example, faces, scene type, person, nudity,person-at-some-pose, action (for video), etc. In addition, it caninclude a data on the region of interest related to this visualclass—for example, the bounding box in which a “Face” has been detected,or the time frame of a “Running Activity”. Finally, the meta-data caninclude a score or a probability corresponding to each visual class. Forexample—a “Face” appears at probability 0.9.

Let C be the set of all visual classes, and let d be some visual entity(e.g.—raw data, or some portion of it). Using the predictabilityframework, we can compute p(d|c) for each cεC. From these likelihoods,we would like to extract the probabilities {p(dεc)}_(cεC), i.e. —theprobability of d to relate to each visual class. These probabilities arecomputed as a function f of the likelihoods: {p(xεc)}_(cεC)=f({p(x|c)}_(cεC)). When the visual classes are mutually exclusive, thefunction f can be set as the simple Bayesian probability:

${p( {x \in c_{0}} )} = \frac{p( x \middle| c_{0} )}{\Sigma_{x}{p( x \middle| c )}}$

In various embodiments of the invention, more general approaches may beimplemented, that use a training set, are based on the SVMclassification or using a model-fitting of the log-likelihoods toextract the probabilities (for example—fitting multi-dimensionalGaussians to the log-likelihood scores, as done in our currentimplementation).

Semantic Tagging

Given the visual meta-data (as discussed above), semantic tags may beattached to the raw data, such as “Nudity”, “Porn”, “Scenery”, etc'.This process is done in the “Semantic Tagging” stage.

A simple way to determine semantic tags from a visual meta-data is byusing a decision tree. For example, saying that an image has “Nudity” init i[p(naked−person)>p₁]f or saying that an image is “Scenery” in highprobability if [p(outdoor)<p₂ & p(people)<p₃]. In addition, it isimportant to notice that a small portion of the visual data may affectthe semantic tagging of the entire data. For example—a single nudeperson appearing in an “innocent” natural scene may cause the entireimage to be tagged as “Nudity” instead of “Nature”.

Such a decision tree may be built manually, based on some applicationdependent strategy. However, according to an embodiment of theinvention, computerized methods may be implemented which can learnautomatically these relations using a training data.

A way to automatically learn the relations between the visual meta-dataand the semantic tags is using SVM. A training set of visual data ismanually tagged, and the meta-data is extracted for this training set.Then, for each semantic tag S, one can automatically learn a detector ofthis tag. For example, learning to determine whether an image is “Porn”based on visual meta-data such as the existence (or the probability) offaces, nudity and certain poses in the image.

Moderation and Decision

The trivial decision scheme is saying that an image or video isinappropriate to watch if it contains “Porn”. However, depending on thecontent moderation application, one might use more complex rules. Someexamples: (a) Different populations might be allowed to watch imageswith deferent porn “severity”. For example—filtering out both nudity andporn for children, while filtering out only “porn” for adults. (b)Different populations may have a totally different definition of a “nonappropriate content”. For example—for many people “non appropriatecontent” consist of only of Pornographic content, while for others itmay consist of images showing body parts (shoulders, legs) or violence(c)_The decision may be based both on the classification results and onthe web site for which the images were taken: e.g. —allowing to show avideo suspicious as “porn” if it appears in a relatively safe web-site,such as news, or blocking it is it appears on YouTube (d) Applyingdifferent actions based on the probability of the image to be “porn”,for example—allow watching it, if the probability is very low, denywatching if the probability is very high, and otherwise letting a humanobserver decide. (e) In some applications, the possible actions are notjust to show or not to show an image, but rather applying other actions,for example—blocking only the problematic regions in the image or video,sending a warning message, or a notice (for example—notifying the parentthat his children is viewing pornographic images).

Fast implementation using GPU

In order to obtain real-time performance, the inventors have implementedthe system in both C++ and on GPU (Graphical Processing Unit). It isnoted that other ways of implementations are available, as will be clearto a person who is of skill in the art. Apart from its usefulness inaccelerating games & graphics, the techniques disclosed were discoveredwherein GPUs was found to be very useful for high-performance imageanalysis. The main components that are the heaviest and needed to beaccelerated are the image-features and the scoring stages).

Classifying videos: video can be classified frame by frame.Alternatively, the entire video can be classified according to visual orsemantic tags extracted for the whole video. For example—saying that avideo is pornographic if it consist of at least one (or a givenpercentage) of pornographic images. An HMM may be used to classifyportions of the video while assuming continuity between frames(consecutive frames tend to belong to the same class). In addition, whenclassifying video content, more cues can be used: (a) Using movementdetection as an ROI (b) Using space-time descriptors (descriptors thatuse pixels from multiple frames), such as motion descriptors.

Separation to Visual Classes and Sub-Classes

Intuitively, in order to detect porn images, one would classify eachimage as being part of one of two classes: “porn” and “non-porn”.However, this approach looses a lot of information, as both the “porn”and “non-porn” classes can be separated to sub-classes, each with itsown statistics. The “porn” class can be separated to sub-classesaccording to pose and/or gender; for example—a standing female, etc'.The “non-porn” class may be first separated to “person” and “non-person”classes, which have very different image statistics (Obviously, the“person” class has much higher confusion with the “porn” than the“non-person” class). The “person” class itself may be separated tosub-classes according to pose and gender. For each of the sub-classes weconstruct a DB of images (/videos) of that sub-classes, which is used toscore the query.

For example, given a visual entity d, a score s_(i) is computed for eachsub-class i (out of a predetermined set of sub-classes), and all thescores of the sub-classes related a category (e.g. “porn”) are combined,e.g.: f_({porn})=f(s₁, . . . , s_(k)). The combination function f canbe, for example, a minimum of all the scores. Similarly, all the scoresof sub-classes related to “non-porn” are combined, and the visual entityd is classified as “porn” if f_({porn})<f_({non-porn}).

Finding Region of Interest (ROI)

One of the major challenges in content moderation is that a relativelysmall portion of the visual data may in some situations significantlyaffect the desired moderation of the entire data. For example, a nudeperson that appears in the image may change its visual class to “Nudity”although the nude person captures a small part of the image. This posesa computer vision challenge of detecting objects inside a largerscene—as an exhaustive search over the entire raw data may not bepractical.

The disclosed solutions may implement various techniques for overcomingthis issue. For example, according to an embodiment of the invention,very fast algorithms may be used for finding ROI candidates, such as:

-   -   i. The whole image is usually selected as a ROI candidate.    -   ii. Homan body detector can be used to select ROI, or based on        face detection.    -   iii. Skin color can be used to select ROI, by selecting large        skin blobs (skin detection is shown in many papers discussing        content filtering, e.g.    -   iv. For video, movement detection can be used to select ROI, by        selecting large blocks that were moving (for example—these block        may indicate a human body, or a moving part of the body)

It is noted that the visual classes will be computed for differentportions of the input media. For example—tagging part of the image as“Adult Content”, or segmenting a video to “Nudity” and “No Nudity”periods.

In order to reduce the computational time required, a set of Cascadesmay be implemented for the system or method. Each cascade extract thevisual meta-data for some portion of the input raw-data (those for whichthe meta-data can easily be extracted) and passes the rest of the inputdata for further examination by other cascades. This mechanism enablesto process “easy” inputs very fast (e.g., by using a partial set of thefeatures, or using a sub-sampled version of the visual data), whilespending more time on “ambiguous” images and videos. This idea isdemonstrated in FIG. 18, when the raw data may include images and thevisual class is “Nudity”.

FIG. 18 illustrates a cascaded process 1800, according to an embodimentof the invention.

The cascaded process may be used in various embodiments of the inventionto reduce the computational time. The cascaded process includes multipleiteration of stages (filters) 1810-1840, each stage may differ from eachother by the level of computational resources it may consume, by itsspeed, by its granularity and the like. An outcome of one iteration canbe fed to another iteration. For example, when searching for mediaobject entities of a certain class, one iteration can be used forperforming a gross filtering operation (may be quicker, run on lowerresolution image entities, use less media class descriptors and thelike). Those media entities that pass the iteration are sent to anotheriteration that may apply a finer filtering process, and may utilize morecomputational resources.

The first stage (first detector) 1810 can run on all images, return aprobability (of belonging to a certain class or classes), if thatprobability is above a first threshold the image is sent to the nextstage—else it is filtered out. It can run on media elements and performthe filtering out on a media element basis.

The second stage (second detector) 1820 can operate on images thatpassed the first stage 1810. It can allocate more resources than thefirst stage 1810. It may apply different calculations or the samecalculation with different probability thresholds to filter out. It canrun on media elements and perform the filtering out on a media elementbasis.

The third stage (second detector) 1830 can operate on images that passedthe second stage 1820. It can allocate more resources than the firststage 1810. It may apply different calculations or the same calculationwith different probability thresholds to filter out. It can run on mediaelements and perform the filtering out on a media element basis.

There can be any number of stages. The last stage can be sent for humanclassification (stage 1840) but this is not necessarily so and theprocess can be fully automated.

FIG. 19 illustrates method 1900 according to an embodiment of theinvention.

Method 1900 may start by stage 1910 of partitioning the image tomultiple media entities or receiving partition information indicative ofa partition of the image to multiple media entities.

Stage 1910 may be followed by multiple iterations of stages 1915, 1920,1930, 1940 and 1945.

Stage 1915 includes selecting a complexity level. A complexity level mayrepresent the time required for completing the iteration, amount ofcomputational resources required to complete the iteration and the like.It is assumed that the lowest complexity is elected first and that thecomplexity increases with the number of iterations.

Stage 1915 is followed by stage 1920 of receiving or generating (a)media class descriptors for each media entity class out of a set ofmedia entity classes, and (b) probability estimations of descriptorspace representatives given each of the media entity classes; whereinthe descriptor space representatives are representative of a set ofmedia entities. The media class descriptors and the probabilityestimations may correspond to the complexity level—each complexity levelmay include its own media class descriptors and the probabilityestimations. Additionally or alternatively, different complexity levelscorrespond to the descriptors of the media entities. The number ofdescriptors of media entities (that represents these media entities) canincrease and thereby increase the complexity level. Additionally oralternatively, the identity of the descriptors of the media entitiesthat are taken into account during each iteration may change.Additionally or alternatively, the set of descriptor types that are usedmay change. For example, using only SIFT in first iteration, and severaldescriptor types in other iterations.

Stage 1920 may be followed by stage 1930 of calculating, for each pairof media entity and media class of a same complexity level, apredictability score based on (a) the probability estimations of thedescriptor space representatives given the media class descriptors ofthe media class, and (b) relationships between descriptors of the mediaentity and the descriptor space representatives.

Stage 1930 may be followed by stage 1940 of classifying each mediaentity of the multiple media entities based on predictability scores ofthe media entity given each media class for the complexity level.

Different complexity levels may differ from each other by an amount ofmedia class descriptors, and additionally or alternatively, an amount ofdescriptors of the media entity.

Different complexity levels may differ from each other by a resolutionof media entities.

Stage 1940 is followed by stage 1950 of determining whether there isanother complexity level to be determined. If the answer is positivethen stage 1950 is followed by stage 1915 during which a new complexitylevel is selected. Stage 1915 may include filtering out irrelevant mediaentities—those who do not belong, according to the currentclassification to one or more predefined classes.

If the answer is negative (no more complexity levels to examine) thenstage 1950 may be followed by stage 1960 of providing at least one imageclassification, based on classes of each of the multiple media entities.

According to an embodiment of the invention method 1900 may includefiltering out by each iteration at least zero media entity—wherein afiltered out media entity is not provided as input to a next iteration.The filtering out includes determining that zero or more media entitiesdo not belong to a certain class of interest and not providing thatfiltered out media entity to the next iteration.

It is noted that during one or more iterations the method can partitionthe image to multiple image entities.

FIG. 20 illustrates method 2000 according to an embodiment of theinvention.

Method 2000 may start by stage 2010 of partitioning the image tomultiple media entities or receiving partition information indicative ofa partition of the image to multiple media entities.

Stage 2010 may be followed by multiple iterations of stages 2015, 2020,2030, 2040 and 2045.

Stage 2015 includes selecting a level of resolution. A level ofresolution can represent a sampling of the media entities that providethe descriptors of the media entities. It is assumed that the lowestresolution is elected first and that the level of resolution increaseswith the number of iterations. Different levels of resolution mayrepresent different numbers of descriptors that are used to representthe media entities.

Stage 2015 is followed by stage 2020 of receiving or generating (a)media class descriptors for each media entity class out of a set ofmedia entity classes, and (b) probability estimations of descriptorspace representatives given each of the media entity classes; whereindescriptor space representatives are representative of a set of mediaentities.

Stage 2020 is followed by stage 2030 of calculating, for each pair ofmedia entity and media class of the level of resolution level, apredictability score based on (a) the probability estimations of thedescriptor space representatives given the media class descriptors ofthe media class of the resolution level, and (b) relationships betweendescriptors of the media entity (for the resolution level) and thedescriptor space representatives.

Stage 2030 is followed by stage 2040 of classifying each media entity ofthe level of resolution based on predictability scores of the mediaentity given each media class for the level of resolution.

Stage 2040 is followed by stage 2050 of determining whether there isanother complexity level to be determined. If the answer is positivethen stage 2050 is followed by stage 2015 during which a new complexitylevel is selected. Stage 2015 may include filtering out irrelevant mediaentities—those who do not belong, according to the currentclassification to one or more predefined classes.

If the answer is negative (no more complexity levels to examine) thenstage 2050 may be followed by stage 2060 of providing at least one imageclassification, based on classes of each of the multiple media entities.

According to an embodiment of the invention method 2000 may includefiltering out by each iteration at least zero media entity—wherein afiltered out media entity is not provided as input to a next iteration.The filtering out includes determining that zero or more media entitiesdo not belong to a certain class of interest and not providing thatfiltered out media entity to the next iteration.

It is noted that during one or more iterations the method can partitionthe image to multiple image entities.

FIG. 21 illustrates method 2100 according to an embodiment of theinvention.

Method 2100 may start by stage 2110 of partitioning the image tomultiple media entities or receiving partition information indicative ofa partition of the image to multiple media entities.

Stage 2110 may be followed by multiple iterations of stages 2115, 2120,2130, 2140 and 2145.

Stage 2115 includes selecting a number of media entity class descriptorsto select out of available media entity media class descriptors. It isassumed that the number of selected media entity increases with thenumber of iterations. It is noted that sets of media entity descriptorsthat differ from each other (not necessarily by number but by theidentity of selected media descriptors) can be elected.

Stage 2115 is followed by stage 2120 of receiving or generating (a)media class descriptors for each media entity class out of a set ofmedia entity classes, and (b) probability estimations of descriptorspace representatives given each of the media entity classes; whereinthe descriptor space representatives are representative of a set ofmedia entities.

Stage 2120 is followed by stage 2130 of calculating, for each pair ofmedia entity and media class, a predictability score based on (a) theprobability estimations of the descriptor space representatives giventhe media class descriptors of the media class, and (b) relationshipsbetween the descriptors of the media entity and the descriptor spacerepresentatives.

Stage 2130 is followed by stage 2140 of classifying each media entity ofthe based on predictability scores of the media entity given each mediaclass for the selected media class descriptors.

Stage 2140 is followed by stage 2150 of determining whether there isanother number of media entity descriptors to be determined. If theanswer is positive then stage 2150 is followed by stage 2115 duringwhich a new number of media class descriptors is selected. Stage 2115may include filtering out irrelevant media entities—those who do notbelong, according to the current classification to one or morepredefined classes.

If the answer is negative (no more classifiers to examine) then stage2150 may be followed by stage 2160 of providing at least one imageclassification, based on classes of each of the multiple media entities.

According to an embodiment of the invention method 2100 may includefiltering out by each iteration at least zero media entity—wherein afiltered out media entity is not provided as input to a next iteration.The filtering out includes determining that zero or more media entitiesdo not belong to a certain class of interest and not providing thatfiltered out media entity to the next iteration.

It is noted that during one or more iterations the method can partitionthe image to multiple image entities.

EXAMPLE

In this sub-section we take a simple example, and describe animplementation the entire flow based on this example.

Assume that we get as an input the image bellow 22A. The visual analysisstage is described in sub-FIGS. 22B-22E. First, image attributes areextracted from am image 2201 that include a house 2202 and a person 2203that stands outside the house. In this simple example, we compute twoimage attributes:

-   -   i. Image gradients (used later by the SIFT descriptor)    -   ii. ROI (region-of-interest) extracted using a face/person        detector. The ROI is marked with grid 2204 in FIG. 22B. The grid        includes grid elements such as four elements 2205.

Next, image features are computed on some set of sample points (in theexample shown in 22B—a descriptor, e.g. SIFT, is computed on a densegrid inside the ROI. Using the set of features (SIFT) extracted from theimage, a likelihood score is computed for every sample point indicatingthe probability of that sample point to belong to the various classes(e.g., the class: “Naked Person”). This likelihood is computed bycomparing each image feature (e.g. SIFT descriptor 2206) with a given DBof features corresponding to this class and measuring its density withinthis class. FIG. 22C illustrates: (a) descriptors of a media class names“naked person” 2207 a and (b) descriptor space representatives (denoted“descriptor representatives) 2207 b, that are fed to a pre-processingblock to provide (c) media descriptors database 2207 c. The mediadescriptors database 2207 and (d) descriptors of the media entity (image2201 of image portion within grid 2204) 2206, are fed to a query blockthat calculates the predictability score.

Schematically, for a hypothetic 2D feature, the density estimation willlook like FIG. 22D. In this sub-figure, it can be seen that the 2Dfeature indicated by the dot is more likely to belong to the “Non-NakedPerson” class (of points 2211) than to the “Naked Person” class ofpoints 2210. The likelihoods from all the image features (descriptors)and from all the classes is combined, proving a probability of the queryregion to belong to each of the classes

As shown in FIGS. 22E and 22F, the result is a meta-data consisting ofthe visual tags 2213 and 2214. The tagging can be probabilistic(“soft”)—as illustrated by box 2220, indicating for example that [p(isperson)=0.9], [p(person is naked)=0.8] and [p(outdoor−scene)=0.95] (notethat the classes can sometimes overlap). Finally, the meta-data (visualtags) is passed to a content moderation & decision blocks (2222) whichuse the visual tags to make a decision 2224 (in this example—Image issuitable for all type of users, as shown in FIG. 22F.

The above techniques may be implemented by various systems, according tovarious embodiments of the invention. A first mode of operation, alsoreferred to as “Server” mode (See FIG. 23), in which there is onlyservers (cache server 2310 having a cache database 2312 and visualanalysis servers 2322 collectively denoted 2320) that include all thecomponents, including the visual analysis component. A second mode ofoperation is a “Client-Server” mode, in which there are both a serverand a Client (See FIGS. 23 and 24). The Client does not have the visualanalysis component (Therefore, to Client extract the visual tags byaccessing the Server), but does include a decision component, which canbe adjusted to a specific user credentials. A final mode of operation isa “Thin Client Mode” (FIG. 25) in which the client is even thinner—givena URL it access the server for an action such as “blocking an image”,etc. (It might also use a local cache).

Server Mode

The server system, according to an embodiment of the invention, isdescribed in FIG. 23. The input to the cache server 2312 and to thevisual analysis servers 2320 is a bulk of visual raw data (e.g.—a set ofimages, or a video steam) or a URL. The visual raw data is first passedthrow a caching mechanism (2310), to avoid un-necessary computations fora visual content that has already been processes. If the visual data isnot found in the caching data base 2312, it is passed through the“Visual Analysis” servers 2320 that extract Semantic Tags 2334 from thevisual data. The details of the “Visual Analysis” component aredescribed in previous sections. Optionally, the visual analysis servers2320 may also get some information about the user. For example—the speedand quality of the processing may differ between users (users for whichreal-time processing is needed, premium users, etc'. User statistics canalso be used as priors for the visual analysis stage).

In either way (using cache, or using the visual analysis component), aset of semantic tags 2334 that correspond to the input visual data areextracted. The semantic tags, together with user credentials 2330, arebeing used as an input to the decision server 2314. The user credentialshelp the decision server 2314 to adjust the decision to a specific user.The decision server runs the content moderation stage, in which thesuitability of the input raw data to various destination populations isdetermined. The decision server may use a memory component in caseswhere earlier decisions are relevant. For example—in the case of inputvideo stream, where the semantic tags and the decisions can beinfluenced by earlier chunks in the same video.

The output of the decision server 2314 is an action decision, forexample—blocking an image or sending a warning. In addition to thesemantic tags, the decision server can use other sources of information:either user credentials (user type & settings, user statistics, etc') orenvironmental sources of information, such as semantic tags extractedfrom textual analysis, or information related to the URL.

Client-Server Mode

A regular client is similar to the Server, in the exception that itdoesn't include the visual analysis component. Instead, the URL is sendto a service (such as server/cloud visual analysis service) 2340 thatdoes the visual analysis and returns back the Semantic Tags 2354. In aregular client, there can be an internal component 2334 that does thedecision itself according to the output of the visual server. Thiscomponent 2334 can include the internal settings of the client (forexample—degree of restriction) and additional information that may helpto make the decision. The local cache of the client can help reducingthe frequency in which the client has to access the visual analysisservice. URLs that are access repeatedly can be handled internally. Thismode is described in FIG. 24. FIG. 24 provides a schematic diagram ofthe Client system. It includes the decision module, but the visualanalysis is done by accessing an external Server. See body of text forfurther details

“Thin Client” Mode

A thin client has even less components—given a URL, it simply access theserver 2512 or a local cache 2510 (that has a local cache data base2512) with the URL, and returns with a decision. A local cache can beused by a thin client too, in order to reduce the frequency ofserver-access. This mode is described in FIG. 25.

Section 7: Applications/Client types

This section describes different type of clients that might use theproposed system. For each type of client the system may be implementedusing each one of the various modes of operation, as described in theprevious section (Server, Client-Server and Thin-Server Modes).

Internet TCP/IP Network Provider/Operator

The proposed system can be used by internet providers to moderate theircontent with respect to their clients. For example—enabling internetproviders to suggest a “porn-free” surfing experience, or moderating thesupplied internet content with respect to the user (e.g. —limited accessfor kids, etc'). These internet users include both users of privatecomputers, cellular users or via TV (for example, with Google TV).

Enterprise

The proposed system can be used by enterprises to filter their visualcontent.

Education

The proposed system can be used by educational facilities (such asschools) to filter the visual content that can be reached by thestudents/pupils.

User Generated Content (UGC)

The proposed system can be used to do content moderation for UserGenerated Content (UGC) sites such as YouTube, Face-book, Flickr, etc'.In such sites, traditional solutions of text analysis are insufficient.In many cases, the visual information is not accompanied with anytextual information. In other cases, misleading textual information isinserted deliberately. This renders textual analysis and domainfiltering useless against these threats. Other methods such as crowdsourcing and collaborative filtering are inefficient for the long tailof visual information in user generated content.

Consumer Client

The proposed system can be used by end-users using a downloadable clientfiltering software on any personal computer (desktop, Laptop, Smartphoneetc.). Lately, the TV can also be used for browsing in the web (e.g.,using Google-TV) and may also be addressed as a personal computer.

Media Stocks

The proposed system can be used to do content moderation in media stocks(such as Google, Yahoo!, iStockPhoto, Getty Images, PhotoBucket, etc').

FIGS. 26 and 27 illustrate a detection of a body organ that is followedby a detection of other body organs. In these figures a face is detected(box 2610 of FIG. 26 and box 2710 of FIG. 27) and then the other bodyorgans are detected (within an estimated upper body area 2730, within anestimated chest area 2720 and within an estimated legs area 2740) basedon a location of the face (box 2612 of FIG. 26). It is noted that thefirst detected body organ can differ from the face of the person. Theestimates areas of the other body organs can be analyzed to detect theseother body organs (such as by classification of each body part—2614) andthis may lead to a classification of the entire image.

The above disclosed systems and methods may also be implemented as acomputer program product, that include non-transitory computer readablemedium having a computer readable medium embodied therein, wherein thecomputer readable medium includes instructions for the carrying out ofthe above stages.

It is noted that the above disclosed methods and systems may beimplemented in various implementations, embodiments, and fields of art,few examples of which were offered above.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

Either one of the mentioned above methods can be executed by a computerprogram product that includes a non-transitory computer readable medium.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

We claim:
 1. A method for classifying at least one image, the methodcomprises: partitioning the at least one image to multiple mediaentities or receiving a partition information indicative of a partitionof the at least one image to multiple media entities; receiving orgenerating (a) media class descriptors for each media entity class outof a set of media entity classes, and (b) probability estimations ofdescriptor space representatives given each of the media entity classes;wherein the descriptor space representatives are representative of a setof media entities; calculating, for each pair of media entity and mediaclass, a predictability score based on (a) the probability estimationsof the descriptor space representatives given the media classdescriptors of the media class, and (b) relationships betweendescriptors of the media entity and the descriptor spacerepresentatives; classifying each media entity of the multiple mediaentities based on predictability scores of the media entity given eachmedia class; and providing at least one image classification, based onclasses of each of the multiple media entities.
 2. The method accordingto claim 1 wherein the at least one image comprises multiple images andwherein the classifying comprising classifying each image.
 3. The methodaccording to claim 1 wherein the at least one image forms a videostream.
 4. The method according to claim 1, wherein the at least oneimage is a single image.
 5. The method according to claim 1, comprisingapplying a human body detection algorithm on the at least one image toprovide human body detection results; and partitioning the at least oneimage based on the human body detection results.
 6. The method accordingto claim 1, comprising applying a face detection algorithm on the atleast one image to provide face detection results; and partitioning theat least one image based on the face detection results.
 7. The methodaccording to claim 1, comprising applying a human skin detectionalgorithm on the at least one image to provide human skin detectionresults; and partitioning the at least one image based on the human skindetection results.
 8. The method according to claim 1, comprisingdetermining whether the at least one image should be prevented frombeing displayed.
 9. The method according to claim 1, comprising defininga dominant class and classifying an image out of the at least one imageas belonging to the dominant class if at least one media entity of theimage is classified as belonging to the dominant class.
 10. The methodaccording to claim 1, comprising executing multiple iterations of asequence of stages that comprises the stages of receiving or generating,calculating, and classifying; wherein different iterations differ fromeach other by accuracy of execution and speed of execution.
 11. Themethod according to claim 10, wherein the decision of whether to applyeach iteration is based on the output of previous iterations.
 12. Themethod according to claim 10, comprising filtering out an image based onan outcome of at least one iteration of the multiple iterations.
 13. Themethod according to claim 1 comprising: detecting a human organ;determining, based on a location of the human organ, an expectedlocation of at least one other human organ; and verifying the expectedlocation of the at least one other human organ by processing at leastone portion of an image that corresponds to the expected location of theat least one other human organ.
 14. The method according to claim 1,comprising executing multiple iterations of a sequence of stages thatcomprises the stages of receiving or generating, calculating, andclassifying; wherein different iterations differ from each other by acomplexity level.
 15. The method according to claim 14, wherein at leasttwo iterations further comprise partitioning the at least one image. 16.The method according to claim 14, comprising performing multipleiterations, each iteration associated with a higher complexity level.17. The method according to claim 14, wherein the decision of whether toapply each iteration is based on the output of previous iterations. 18.The method according to claim 1, comprising executing multipleiterations of a sequence of stages that comprises the stages ofreceiving or generating, calculating, and classifying; wherein differentiterations differ from each other by a number of descriptors of themedia entities.
 19. The method according to claim 17, wherein at leasttwo iterations further comprise partitioning the at least one image. 20.The method according to claim 10, comprising performing multipleiterations while increasing the number of descriptors of the mediaentities.
 21. The method according to claim 1, comprising executingmultiple iterations of a sequence of stages that comprises the stages ofreceiving or generating, calculating, and classifying; wherein differentiterations differ from each other by a selection of media classdescriptors that are taken into account during the iteration.
 22. Themethod according to claim 18, wherein at least two iterations furthercomprise partitioning the at least one image.
 23. The method accordingto claim 1, comprising executing multiple iterations of a sequence ofstages that comprises the stages of receiving or generating,calculating, and classifying; wherein different iterations differ fromeach other by descriptors of the media entity that are taken intoaccount during the iteration.
 24. The method according to claim 23,wherein at least two iterations further comprise partitioning the atleast one image.
 25. The method according to claim 1, comprisingapplying a motion detection algorithm on the at least one image toprovide motion detection results; and partitioning the at least oneimage based on the motion detection results.
 26. A computer programproduct that comprises a non-transitory computer readable medium thatstores instructions for: partitioning the at least one image to multiplemedia entities or receiving a partition information indicative of apartition of the at least one image to multiple media entities;receiving or generating (a) media class descriptors for each mediaentity class out of a set of media entity classes, and (b) probabilityestimations of descriptor space representatives given each of the mediaentity classes; wherein the descriptor space representatives arerepresentative of a set of media entities; calculating, for each pair ofmedia entity and media class, a predictability score based on (a) theprobability estimations of the descriptor space representatives giventhe media class descriptors of the media class, and (b) relationshipsbetween descriptors of the media entity and the descriptor spacerepresentatives; classifying each media entity of the multiple mediaentities based on predictability scores of the media entity given eachmedia class; and providing at least one image classification, based onclasses of each of the multiple media entities.